Feature Engineering is an important step to develop and improve performance of Machine Learning models. In this course, students will learn different data wrangling techniques that help transforming the raw data to an appropriate form on which learning algorithms can be applied.

This course enables students to identify and implement appropriate feature extraction and pre-processing techniques. The Visualization techniques will also be taught in this course.

Overview of Feature Engineering Data Pre-processing Dimensionality Reduction Visualization

The course on Classification lays down a strong foundation on the algorithmic perspective of popular classification algorithms - k-NN, Naïve Bayes, Decision Tree, Logistic Regression and SVM. The implementation details of these models along with tuning of parameters will be illustrated. The course also covers concepts such as ensemble methods like bagging, boosting, Random Forest, and interpretability of the predicted models.

Overview of the Classification ModuleNearest-neighbour MethodsNaïve Bayes ClassifierLogistic RegressionDecision TreeOptimization Foundations for Support Vector MachinesSupport Vector MachinesSupport Vector Machines in overlapping class distributions & KernelsEnsemble Methods

The course on Unsupervised Learning & Association Rule Mining focuses in finding natural groups or clusters that are present in the data. The course will cover lustering algorithms like K-means, Hierarchical & DBSCAN algorithms, Hidden Markov Models for time series prediction, and market basket analysis to generate the interesting rules from a transactional database.

Introduction to Unsupervised Learning, ClusteringK-Means Algorithm, K-Means – Variations, Detecting OutliersMath Fundamentals for EM Algorithm, EM Algorithm, Clustering for Customer SegmentationHierarchical ClusteringDensity Based Clustering, Clustering for Anomaly DetectionAssessing Quality of Clustering, Significance of Clustering - Interpreting/ summarizing Clusters by businessesAssociation Rule Mining, Apriori AlgorithmTime series Prediction and Markov Process, Hidden Markov Model

Text mining is the process of deriving high-quality information from text and this is the fifth course of the program. This course aims to equip students with adequate knowledge in extracting the relevant text data and skills to identify patterns therein. This course covers topics like converting documents to vectors, Parts of Speech Tagging, Topic modelling, sentiment analysis and recommender systems.

Document vectorization and Parts of Speech TaggingIntroduction to Part of speech tagging, Part of speech tagging using HMM-1, Implementing POS Tagging in PythonTopic modelling using LDAIntroduction to Sentiment AnalysisRecommender Systems

Deep learning is an evolving subfield of Machine Learning and this course starts with traditional Neural Networks followed by sequential networks, Convolution Networks, Autoencoders and Generative deep learning models. The implementation details of these deep learning models along with tuning of the parameters will be illustrated in this course.

Artificial Neural Network Sequence Modeling in Neural Network Deep Learning Convolution Networks with Deep Learning Autoencoders with Deep Learning Generative deep learning models

During the 8-week Capstone Project, participants will work in teams to design and solve a real-world business problem encompassing data science pipeline using AI&ML techniques under the mentorship of Nextgen Education Foundation faculty members and senior Industry practitioners.

In addition to the Curriculum above, participants will have the option of taking an optional course on Python at the beginning of the Programme. This will allow participants to revisit essential concepts that will help in all other courses during the programme. Topics covered include Introduction to Python programming and installation, Data Types, Program constructs, Numpy, Pandas, Matplotlib, and Debugging python programs.